Recent Posts - Page 22#
Fine-tuning Llama 3 with Axolotl using ROCm on AMD GPUs
This blog demonstrates how to fine-tune Llama 3 with Axolotl using ROCm on AMD GPUs, and how to evaluate the performance of your LLM before and after fine-tuning.
Inferencing and serving with vLLM on AMD GPUs
Learn step-by-step how to leverage vLLM for high-performance inferencing and model serving on AMD GPUs
Enhancing vLLM Inference on AMD GPUs
Showcases the latest performance enhancements in vLLM inference on AMD Instinct accelerators using ROCm 6.2, including FP8 KV-Cache, quantization, and GEMM tuning
Getting to Know Your GPU: A Deep Dive into AMD SMI
This post introduces AMD System Management Interface (amd-smi), explaining how you can use it to access your GPU’s performance and status data
Presenting and demonstrating the use of the ROCm Offline Installer Creator, a tool enabling simple deployment of ROCm in disconnected environments in high-security environments and air-gapped networks.
Presenting and demonstrating the use of the ROCm Offline Installer Creator, a tool enabling simple deployment of ROCm in disconnected environments in high-security environments and air-gapped networks.
Optimize GPT Training: Enabling Mixed Precision Training in JAX using ROCm on AMD GPUs
Guide to modify our JAX-based nanoGPT model for mixed-precision training, optimizing speed and efficiency on AMD GPUs with ROCm.
Image Classification with BEiT, MobileNet, and EfficientNet using ROCm on AMD GPUs
Image Classification with BEiT, MobileNet, and EfficientNet on AMD GPU
Seismic stencil codes - part 3
Seismic Stencil Codes - Part 3: In the last two blog posts, we developed a HIP kernel capable of computing high order finite differences commonly needed in seismic wave propagation.
Seismic stencil codes - part 1
Seismic Stencil Codes - Part 1: Seismic workloads in the HPC space have a long history of being powered by high-order finite difference methods on structured grids. This trend continues to this day.
Seismic stencil codes - part 2
Seismic Stencil Codes - Part 2: In the previous post, recall that the kernel with stencil computation in the z-direction suffered from low effective bandwidth. This low performance comes from generating substantial amounts of data to movement to global memory.
Benchmarking Machine Learning using ROCm and AMD GPUs: Reproducing Our MLPerf Inference Submission
Benchmarking Machine Learning using ROCm and AMD GPUs: Reproducing Our MLPerf Inference Submission
Performing natural language processing tasks with LLMs on ROCm running on AMD GPUs
Performing natural language processing tasks with LLMs on ROCm running on AMD GPUs